tokenizer-go VS tiktoken

Note: there is another library that does something very similar but it isn't pure go, it uses a npm package and invokes nodejs to encode/decode.

Their tokenizer is open source: https://github.com/openai/tiktoken

Data files that contain vocabulary are listed here: https://github.com/openai/tiktoken/blob/9e79899bc248d5313c7d...

OpenAI's refference tokeniser - https://github.com/openai/tiktoken

ChatGPT presumably adds them as special tokens to the cl100k_base tokenizer, as they demo in the tiktoken documentation: https://github.com/openai/tiktoken#extending-tiktoken

I want to measure the approximate number of tokens in a piece of text to understand if I will need to modify it before passing it into the context of an OpenAI API call. Tiktoken can do this, but I'm not sure if it's overkill to use that library just for this simple task. I don't need to actually tokenize the text, I just need an approximate count (e.g. within like 1% of the text's actual token length for text that represents the visible text on a webpage).

https://platform.openai.com/tokenizer or the official python library tiktoken https://github.com/openai/tiktoken or this JS port of tiktoken https://github.com/dqbd/tiktoken

It's built on top of the tiktoken library and is basically just a lambda function in the backend.

"All" is a single token in OpenAI's tiktoken Tokenizer, unrelated to the token for capital "A". Even lowercase "all" is a distinct token from "All" or "ALL."

check tiktoken